Multivariate Bayesian time series models

There are currently two areas of research in this topic.

Multivariate dynamic linear models

Research in this area focuses on the theoretical and practical development of Bayesian dynamic graphical models, which combine graphical models with Bayesian state space models. They have been developed to model and forecast multivariate time series. The models represent any conditional independences related to causality which may exist across the multivariate time series by a graph, which then breaks the multivariate problem into simpler sub-problems. As a consequence, even though Bayesian dynamic graphical models can model what can be highly complex multivariate relationships, they are computationally straightforward.

These models are currently being developed for forecasting multivariate time series of traffic flows. They are, however, potentially suitable for any application involving flows, such as electricity flows, signal flows in telecommunication networks, flows of packages over the internet, flows of goods in supply chains, and so on.

Bayesian modelling of space-time processes

This research investigates Bayesian forecasting models for realisations of time series processes in space that account for both temporal and spatial correlations.